The term segmentation, as used herein, refers to the identification of boundaries of biological units, such as cells, within a digital image. The digital image may be obtained using a microscope. Weak or data driven segmentations may be used to define cell boundaries. For example, a watershed transform is one image processing technique that has been used for segmenting images of cells. With the watershed transform, a digital image may be modeled as a three-dimensional topological surface, where values of pixels (e.g., brightness or grey level) in the image represent geographical heights. Imaginary rain falling on the surface of the three-dimensional image collects in areas called catchment basins (i.e., valleys, troughs or low points of the surface), and ridge lines become visible above the waterline at a given flooding level. The ridge lines may represent the boundaries of cells in the image at the flooding level, thus providing reference points for segmenting the image.
Due to variations in the shape and composition of each cell, however, weak or data driven segmentations may not produce an accurate segmentation without significant adaptation and optimization to specific tissue type applications. For example, a weak segmentation algorithm may cause the image to be over-segmented (e.g., what appears as a single cell may actually be only a portion of a cell) or under-segmented (e.g., what appears as a single cell may actually be several different cells in combination). Furthermore, the image may not be properly segmented with a weak segmentation algorithm, in part, because a suitable segmentation parameter for one region of the image may not work well in other regions of the same image.